The bandwidth shortage increasingly experienced by mobile carriers has motivated the exploration of the underutilized Millimeter Wave (mmWave) frequency spectrum between 3G and 300G Hz for the next generation broadband cellular communication networks. The available spectrum of mmWave band is hundreds of times greater than the conventional cellular system. The mmWave wireless network uses directional communications with narrow beams and can support multi-gigabit data rate. The underutilized bandwidth of the mmWave spectrum has wavelengths ranging from 1 mm to 100 mm. The very small wavelengths of the mmWave spectrum enable large number of miniaturized antennas to be placed in a small area. Such miniaturized antenna system can produce high beamforming gains through electrically steerable arrays generating directional transmissions. With recent advances in mmWave semiconductor circuitry, mmWave wireless system has become a promising solution for real implementation. However, the heavy reliance on directional transmissions and the vulnerability of the propagation environment present particular challenges for the mmWave network with beamforming.
In principle, beam training mechanism, which includes both initial beam alignment and subsequent beam tracking, ensures that base station (BS) beam and user equipment (UE) beam are aligned for data communication. To ensure beam alignment, beam-tracking operation should be adapted in response to channel changes. However, in mmWave systems, transmission path lifetime is expected one order of magnitude shorter than traditional cellular bands due to wavelength difference. Combined with dedicated beam with small spatial coverage, the number of effective transmission paths for a dedicated beam could be rather limited, thus more vulnerable to UE movements and environmental changes.
For beamformed access, both ends of a link need to know which beamformers to use. In downlink (DL)-based beam management, the BS side provides opportunities for UE to measure beamformed channel of different combinations of BS beams and UE beams. For example, BS performs periodic beam sweeping with reference signal (RS) carried on individual BS beams. UE can collect beamformed channel state by using different UE beams, and UE then report the collect information to BS. Apparently, UE has the most up-to-date beamformed channel state in DL-based beam management. BS learns the beamformed channel state based on UE feedback, and the feedback may include only strong beam pair links selected by UE.
Beam failure recovery mechanism is designed to handle the rare case beam tracking issue, e.g., when feedback rate for beam management may not be frequent enough. Beam recovery mechanism comprises triggering condition evaluation including beam failure detection and candidate beam identification, beam recovery request transmission, and network reaction monitoring. Details of the beam failure recovery procedures need to be carefully designed to shorten the recovery delay while ensure the robustness.